• Journal of Korean Medicine Rehabilitation
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• v.21 no.2
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• pp.1-13
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• 2011

Objectives : We investigated the effect of Haein-tang(Hairen-tang) on short-term memory and apoptosis in dentate gyrus of the gerbils with transient global ischemia. Methods : For the induction of cerebral ischemia model in mice, common carotid arteries of gerbils were occluded with aneurysm clips for 5 min. One day after operation, Haein-tang(Hairen-tang) was administrated orally injected once a day for 15 consecutive days. Gerbils were randomly divided into four group(n=10 in each group): sham-operation group, ischemia-induction group, ischemia-induction and 50 mg/kg Haein-tang(Hairen-tang)-treated group, ischemia-induction and 100 mg/kg Haein-tang(Hairen-tang)-treated group, and ischemia-induction and 200 mg/kg Haein-tang(Hairen-tang)-treated group. The effect of Haein-tang(Hairen-tang) on memory function was investigated by using step-down avoidance task. Apoptosis was confirmed by terminal deoxynucleotidyl transferase-mediated dUTP nick end labeling(TUNEL) staining and immunohistochemistry for caspase-3. Western blot analysis for the expressions of Bax and Bcl-2 protein was also conducted. Results : 1. Haein-tang extract significantly enhanced short-term memory in step-down avoidance task and 100 mg/kg, 200 mg/kg Haein-tang-treated group. 2. Haein-tang extract significantly suppressed TUNEL-positive cells after transient global ischemia and 50 mg/kg, 100 mg/kg, 200 mg/kg Haein-tang-treated group. 3. Haein-tang extract significantly increased caspase-3 positive cells in the hippocampal dentate gyrus after transient global ischemia and 50 mg/kg, 100 mg/kg, 200 mg/kg Haein-tang-treated group. 4. Haein-tang extract significantly decreased Bax protein expressions in the hippocampus after transient global ischemia and 100 mg/kg, 200 mg/kg, Haein-tang-treated group. Haein-tang extract significantly increased Bcl-2 protein expressions in the hippocampal dentate gyrus after transient global ischemia and 50 mg/kg, 100 mg/kg, 200 mg/kg, Haein-tang-treated group. Haein-tang extract significantly decreased Ratio of Bax protein to Bcl-2 protein in the hippocampus after transient global ischemia and 100 mg/kg, 200 mg/kg Haein-tang-treated group. Conclusions : While Haein-tang(Hairen-tang) treatment improved short-term memory by suppressing on ischemia-induction apoptosis. In the present study, Haein-tang(Hairen-tang) shows protective effect on transient global ischemia.

• Journal of the military operations research society of Korea
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• v.11 no.1
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• pp.33-46
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• 1985

This study develops a scientific method in pilot selection by analysing a divided attention performance between the successful pilots and the failures in a flight training course. To measure the divided attention performance, Dual Task Method is used in which the primary task is a tracking task while the secondary tasks are, 1. short-term memory task 2. choice reaction task 3. judgement task. Result shows that the performance of the pilots is significantly better (p < 0.1) than that of the failures in divided attention performance. In addition, the differences in the divided attention performance between the two groups are increased in proportion to the difficulty of the task and especially in the short term memory, the increment is most dramatic.

• Journal of Korean Institute of Industrial Engineers
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• v.10 no.2
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• pp.3-16
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• 1984

This study develops a scientific method in pilot selection by analysing a divided attention performance between the successful pilots and the failures in a flight training course. To measure the divided attention performance, Dual Task Method is used in which the primary task is a tracking task while the secondary tasks are, 1. short term memory task, 2. choice reaction task and 3. judgement task. Result shows that the performance of the pilots is significantly better (P < 0.1) than that of the failures in dual performance. In addition, the differences in the divided attention performance between the two groups are increased in proportion to the difficulty of the task and especially in the Short Term Memory, the increment is most dramatic.

• Annual Conference on Human and Language Technology
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• 2018.10a
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• pp.559-560
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• 2018

순환 신경망(RNN) 기반의 Long Short-Term Memory(LSTM)는 자연어처리 분야에서 우수한 성능을 보이는 모델이다. 음성을 문자로 변환해주는 Speech to Text (STT)를 이용해 자막을 생성하고, 생성된 자막을 다른 언어로 동시에 번역을 해주는 서비스가 활발히 진행되고 있다. STT를 사용하여 자막을 추출하는 경우에는 마침표가 없이 전부 연결된 문장이 생성되기 때문에 정확한 번역이 불가능하다. 본 논문에서는 영어자막의 자동 번역 시, 정확도를 높이기 위해 텍스트를 문장으로 분할하여 마침표를 생성해주는 방법을 제안한다. 이 때, LSTM을 이용하여 데이터를 학습시킨 후 테스트한 결과 62.3%의 정확도로 마침표의 위치를 예측했다.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06b
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• pp.321-323
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• 2012

스마트폰 사용의 보편화와 센서기술의 발달로 이를 응용하는 다양한 연구가 진행되고 있다. 특히 가속도, GPS, 조도, 방향센서 등의 센서들이 스마트폰에 부착되어 출시되고 있어서, 이를 이용한 상황인지, 행동인식 등의 관련 연구들이 활발하다. 하지만 다양한 클래스를 분류하면서 높은 인식률을 유지하는 것은 어려운 문제이다. 본 논문에서는 인식률 향상을 위해 계층적 구조의 순환 신경망을 이용하여 제스처를 인식한다. 스마트폰의 가속도 센서를 이용하여 사용자의 제스처 데이터를 수집하고 BLSTM(Bidirectional Long Short-Term Memory) 구조의 순환신경망을 계층적으로 사용하여, 20가지 사용자의 제스처와 비제스처를 분류한다. 약 24,850개의 시퀀스 데이터를 사용하여 실험한 결과, 기존 BLSTM은 평균 89.17%의 인식률을 기록한 반면 계층적 BLSTM은 평균 91.11%의 인식률을 나타내었다.

• Journal of the Ergonomics Society of Korea
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• v.9 no.1
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• pp.15-20
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• 1990

In order to design man-machine system, communication system and other tasks that require information, we need to understand the characteristics of hyman short-term memory (STM). Thus, the purpose of this thesis is to investigate the influences of information stress and retention time on human performances and their relation- ships for STM of visual invormation. Eight subjects performed the computer monitering with STM task. The results showed that performance on serial recall from STM get wores and response time (and completion time) on information transmission by recall from STM increase as information stress and retention time increase. Also, there existed inverse proportional relationship between recall performance and response time (and completion time).

• Journal of Integrative Natural Science
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• v.11 no.4
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• pp.204-208
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• 2018

Comparison of different optimizer performance in photovoltaic power modeling using artificial neural deep learning techniques is described in this paper. Six different deep learning optimizers are tested for Long-Short-Term Memory networks in this study. The optimizers are namely Adam, Stochastic Gradient Descent, Root Mean Square Propagation, Adaptive Gradient, and some variants such as Adamax and Nadam. For comparing the optimization techniques, high and low fluctuated photovoltaic power output are examined and the power output is real data obtained from the site at Mokpo university. Using Python Keras version, we have developed the prediction program for the performance evaluation of the optimizations. The prediction error results of each optimizer in both high and low power cases shows that the Adam has better performance compared to the other optimizers.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.01a
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• pp.387-388
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• 2019

As malicious apps vary significantly across Android malware, it is challenging to prevent that the end-users download apps from unsecured app markets. In this paper, we propose an approach to classify the malicious methods based on permissions using Long Short Term Memory (LSTM) that is used to embed the semantics among Intent and permissions. Then the malicious method that is an unsecured method will be removed and re-uploaded to official market. This approach may induce that the end-users download apps from official market in order to reduce the risk of attacks.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.07a
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• pp.385-386
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• 2019

This paper proposes a fusion model based on Long-Short Term Memory networks (LSTM) and CNN deep learning methods, and applied to multi-category news datasets, and achieved good results. Experiments show that the fusion model based on deep learning has greatly improved the precision and accuracy of text sentiment classification.

• ETRI Journal
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• v.41 no.5
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• pp.560-573
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• 2019

Two supervised learning algorithms, a basic neural network and a long short-term memory recurrent neural network, are applied to traffic including DDoS attacks. The joint effects of preprocessing methods and hyperparameters for machine learning on performance are investigated. Values representing attack characteristics are extracted from datasets and preprocessed by two methods. Binary classification and two optimizers are used. Some hyperparameters are obtained exhaustively for fast and accurate detection, while others are fixed with constants to account for performance and data characteristics. An experiment is performed via TensorFlow on three traffic datasets. Three scenarios are considered to investigate the effects of learning former traffic on sequential traffic analysis and the effects of learning one dataset on application to another dataset, and determine whether the algorithms can be used for recent attack traffic. Experimental results show that the used preprocessing methods, neural network architectures and hyperparameters, and the optimizers are appropriate for DDoS attack detection. The obtained results provide a criterion for the detection accuracy of attacks.